Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [58]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [59]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[59]:
<matplotlib.image.AxesImage at 0x7f5844937550>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [60]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[60]:
<matplotlib.image.AxesImage at 0x7f581952fa90>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [61]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [62]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    input_real = tf.placeholder(tf.float32,(None,image_height,image_width,image_channels),name="input_real")
    input_z = tf.placeholder(tf.float32,(None,z_dim),name="input_z")
    learning_rate_pl =tf.placeholder(tf.float32,name="learning_rate")
    return input_real,input_z,learning_rate_pl




"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [63]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.10 #TODO: Check
    with tf.variable_scope("discriminator", reuse=reuse):
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        conv1 = tf.maximum(alpha * conv1, conv1)
        
        conv2 = tf.layers.conv2d(conv1, 128, 5, strides=2, padding='same')
        conv2 = tf.layers.batch_normalization(conv2,  training=True)
        conv2 = tf.maximum(alpha * conv2, conv2)
        
        conv3 = tf.layers.conv2d(conv2, 256, 5, strides=2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3,  training=True)
        conv3 = tf.maximum(alpha * conv3, conv3)
   
        conv4 = tf.layers.conv2d(conv3, 512, 5, strides=1, padding='same')
        conv4 = tf.layers.batch_normalization(conv3,  training=True)
        conv4 = tf.maximum(alpha * conv3, conv3)

        flat  = tf.reshape(conv4, (-1, 4*4*512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [64]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2 #TODO:
    with tf.variable_scope("generator", reuse=not is_train):
        x = tf.layers.dense(z, 7*7*512)
        x = tf.reshape(x, (-1, 7, 7, 512))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        
        conv1 = tf.layers.conv2d_transpose(x, 256, 5, strides=1, padding="same")
        conv1 = tf.layers.batch_normalization(conv1, training=is_train)
        conv1 = tf.maximum(alpha * conv1, conv1)
        
        conv2 = tf.layers.conv2d_transpose(conv1, 128, 5, strides=1, padding="same")
        conv2 = tf.layers.batch_normalization(conv2, training=is_train)
        conv2 = tf.maximum(alpha * conv2, conv2)
       
        conv3 = tf.layers.conv2d_transpose(conv2, 128, 5, strides=2, padding="same")
        conv3 = tf.layers.batch_normalization(conv3, training=is_train)
        conv3 = tf.maximum(alpha * conv3, conv3)
                
        logits = tf.layers.conv2d_transpose(conv3, out_channel_dim, 5, strides=2, padding="same")
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [65]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    #TODO:??
    gen_model = generator(input_z,out_channel_dim,is_train=True)
    
    d_model_real,d_logits_real =discriminator(input_real,reuse=False)
    d_model_fake,d_logits_fake =discriminator(gen_model,reuse=True)
    
    d_loss_real = tf.reduce_mean(
                  tf.nn.sigmoid_cross_entropy_with_logits(
                  logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(
                  tf.nn.sigmoid_cross_entropy_with_logits(
                  logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
             tf.nn.sigmoid_cross_entropy_with_logits(
             logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [66]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    #TODO:??
    train_vars = tf.trainable_variables()
    d_vars = [var for var in train_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in train_vars if var.name.startswith('generator')]
    
    d_train_opt = tf.train.AdamOptimizer(
                  learning_rate, beta1=beta1).minimize(
                  d_loss, var_list=d_vars)
    with tf.control_dependencies(
         tf.get_collection(tf.GraphKeys.UPDATE_OPS)): 
        g_train_opt = tf.train.AdamOptimizer(
                      learning_rate, beta1=beta1).minimize(
                      g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [67]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [77]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    n_samples, width, height, channels = data_shape
    input_real, input_z, learn_rate = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    show_every = 100 #TODO:
    print_every = 100
    

    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images = batch_images * 2.0
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})

                if steps % show_every == 0:
                    n_images = 16
                    show_generator_output(sess, n_images, input_z, channels, data_image_mode)

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                steps += 1               
                
         

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [78]:
#TODO:
batch_size = 16
z_dim = 100
learning_rate = 0.005
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 0/2... Discriminator Loss: 5.8722... Generator Loss: 0.0100
Epoch 0/2... Discriminator Loss: 1.7652... Generator Loss: 0.3876
Epoch 0/2... Discriminator Loss: 1.1299... Generator Loss: 0.7140
Epoch 0/2... Discriminator Loss: 1.5348... Generator Loss: 0.5843
Epoch 0/2... Discriminator Loss: 1.7709... Generator Loss: 0.5768
Epoch 0/2... Discriminator Loss: 1.4595... Generator Loss: 0.6161
Epoch 0/2... Discriminator Loss: 1.3118... Generator Loss: 0.6082
Epoch 0/2... Discriminator Loss: 1.3442... Generator Loss: 0.7767
Epoch 0/2... Discriminator Loss: 1.4613... Generator Loss: 0.6902
Epoch 0/2... Discriminator Loss: 1.1618... Generator Loss: 0.8434
Epoch 0/2... Discriminator Loss: 1.5055... Generator Loss: 1.4437
Epoch 0/2... Discriminator Loss: 1.7123... Generator Loss: 0.3781
Epoch 0/2... Discriminator Loss: 1.4262... Generator Loss: 0.5334
Epoch 0/2... Discriminator Loss: 1.1476... Generator Loss: 0.7834
Epoch 0/2... Discriminator Loss: 0.8471... Generator Loss: 1.9112
Epoch 0/2... Discriminator Loss: 1.5640... Generator Loss: 0.4283
Epoch 0/2... Discriminator Loss: 1.6180... Generator Loss: 0.5104
Epoch 0/2... Discriminator Loss: 1.1986... Generator Loss: 0.7228
Epoch 0/2... Discriminator Loss: 1.0680... Generator Loss: 0.8439
Epoch 0/2... Discriminator Loss: 1.3693... Generator Loss: 0.6017
Epoch 0/2... Discriminator Loss: 2.3494... Generator Loss: 0.1959
Epoch 0/2... Discriminator Loss: 1.6353... Generator Loss: 0.3556
Epoch 0/2... Discriminator Loss: 3.1260... Generator Loss: 0.0807
Epoch 0/2... Discriminator Loss: 2.4906... Generator Loss: 0.1513
Epoch 0/2... Discriminator Loss: 1.2752... Generator Loss: 0.5976
Epoch 0/2... Discriminator Loss: 1.7752... Generator Loss: 0.3460
Epoch 0/2... Discriminator Loss: 1.2447... Generator Loss: 0.6553
Epoch 0/2... Discriminator Loss: 2.1363... Generator Loss: 0.2885
Epoch 0/2... Discriminator Loss: 1.2889... Generator Loss: 0.6894
Epoch 0/2... Discriminator Loss: 0.7188... Generator Loss: 1.3323
Epoch 0/2... Discriminator Loss: 0.7736... Generator Loss: 1.2429
Epoch 0/2... Discriminator Loss: 2.7995... Generator Loss: 0.1180
Epoch 0/2... Discriminator Loss: 1.2244... Generator Loss: 0.6542
Epoch 0/2... Discriminator Loss: 2.0428... Generator Loss: 0.2633
Epoch 0/2... Discriminator Loss: 2.1991... Generator Loss: 0.2778
Epoch 0/2... Discriminator Loss: 1.2061... Generator Loss: 0.6413
Epoch 0/2... Discriminator Loss: 0.9082... Generator Loss: 0.9867
Epoch 0/2... Discriminator Loss: 1.2185... Generator Loss: 0.6155
Epoch 1/2... Discriminator Loss: 1.0277... Generator Loss: 0.9792
Epoch 1/2... Discriminator Loss: 1.6091... Generator Loss: 0.3801
Epoch 1/2... Discriminator Loss: 1.7990... Generator Loss: 0.3558
Epoch 1/2... Discriminator Loss: 1.6331... Generator Loss: 0.3947
Epoch 1/2... Discriminator Loss: 2.9507... Generator Loss: 0.1354
Epoch 1/2... Discriminator Loss: 1.5762... Generator Loss: 0.3735
Epoch 1/2... Discriminator Loss: 1.2719... Generator Loss: 0.7871
Epoch 1/2... Discriminator Loss: 1.9671... Generator Loss: 0.2599
Epoch 1/2... Discriminator Loss: 1.6923... Generator Loss: 0.3806
Epoch 1/2... Discriminator Loss: 2.3478... Generator Loss: 0.2804
Epoch 1/2... Discriminator Loss: 1.1645... Generator Loss: 0.6520
Epoch 1/2... Discriminator Loss: 0.7199... Generator Loss: 1.5313
Epoch 1/2... Discriminator Loss: 1.4079... Generator Loss: 0.5748
Epoch 1/2... Discriminator Loss: 2.2614... Generator Loss: 0.2555
Epoch 1/2... Discriminator Loss: 1.6663... Generator Loss: 0.4149
Epoch 1/2... Discriminator Loss: 1.8148... Generator Loss: 0.3224
Epoch 1/2... Discriminator Loss: 1.4570... Generator Loss: 0.4900
Epoch 1/2... Discriminator Loss: 1.6270... Generator Loss: 0.4658
Epoch 1/2... Discriminator Loss: 1.1118... Generator Loss: 0.8771
Epoch 1/2... Discriminator Loss: 1.5741... Generator Loss: 0.3708
Epoch 1/2... Discriminator Loss: 1.7130... Generator Loss: 0.3868
Epoch 1/2... Discriminator Loss: 2.0082... Generator Loss: 0.2787
Epoch 1/2... Discriminator Loss: 1.5894... Generator Loss: 0.5102
Epoch 1/2... Discriminator Loss: 2.8756... Generator Loss: 0.1141
Epoch 1/2... Discriminator Loss: 1.3046... Generator Loss: 0.8034
Epoch 1/2... Discriminator Loss: 1.5739... Generator Loss: 0.5991
Epoch 1/2... Discriminator Loss: 2.3052... Generator Loss: 0.1874
Epoch 1/2... Discriminator Loss: 2.2238... Generator Loss: 0.2733
Epoch 1/2... Discriminator Loss: 2.8087... Generator Loss: 0.1216
Epoch 1/2... Discriminator Loss: 1.5468... Generator Loss: 0.4219
Epoch 1/2... Discriminator Loss: 2.4382... Generator Loss: 0.2345
Epoch 1/2... Discriminator Loss: 2.1549... Generator Loss: 0.2493
Epoch 1/2... Discriminator Loss: 1.2039... Generator Loss: 0.7804
Epoch 1/2... Discriminator Loss: 1.9106... Generator Loss: 0.3682
Epoch 1/2... Discriminator Loss: 1.2202... Generator Loss: 0.6402
Epoch 1/2... Discriminator Loss: 0.8563... Generator Loss: 1.3054
Epoch 1/2... Discriminator Loss: 1.0356... Generator Loss: 0.8853

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [79]:
batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 0/1... Discriminator Loss: 9.8797... Generator Loss: 0.0001
Epoch 0/1... Discriminator Loss: 2.1086... Generator Loss: 0.3868
Epoch 0/1... Discriminator Loss: 1.8268... Generator Loss: 0.4744
Epoch 0/1... Discriminator Loss: 1.5011... Generator Loss: 0.6684
Epoch 0/1... Discriminator Loss: 1.7638... Generator Loss: 0.4416
Epoch 0/1... Discriminator Loss: 1.4901... Generator Loss: 0.6363
Epoch 0/1... Discriminator Loss: 1.9161... Generator Loss: 0.4536
Epoch 0/1... Discriminator Loss: 1.5477... Generator Loss: 0.6396
Epoch 0/1... Discriminator Loss: 1.8237... Generator Loss: 0.4526
Epoch 0/1... Discriminator Loss: 1.6974... Generator Loss: 0.5395
Epoch 0/1... Discriminator Loss: 1.5771... Generator Loss: 0.6092
Epoch 0/1... Discriminator Loss: 1.4882... Generator Loss: 0.6714
Epoch 0/1... Discriminator Loss: 1.5600... Generator Loss: 0.7111
Epoch 0/1... Discriminator Loss: 1.5697... Generator Loss: 0.5884
Epoch 0/1... Discriminator Loss: 1.5651... Generator Loss: 0.6229
Epoch 0/1... Discriminator Loss: 1.6020... Generator Loss: 0.5890
Epoch 0/1... Discriminator Loss: 1.6978... Generator Loss: 0.5380
Epoch 0/1... Discriminator Loss: 1.6779... Generator Loss: 0.4806
Epoch 0/1... Discriminator Loss: 1.4024... Generator Loss: 0.7551
Epoch 0/1... Discriminator Loss: 1.7013... Generator Loss: 0.5757
Epoch 0/1... Discriminator Loss: 1.6490... Generator Loss: 0.5325
Epoch 0/1... Discriminator Loss: 1.5152... Generator Loss: 0.6758
Epoch 0/1... Discriminator Loss: 1.5919... Generator Loss: 0.6179
Epoch 0/1... Discriminator Loss: 1.6895... Generator Loss: 0.5608
Epoch 0/1... Discriminator Loss: 1.8145... Generator Loss: 0.4797
Epoch 0/1... Discriminator Loss: 1.4549... Generator Loss: 0.6863
Epoch 0/1... Discriminator Loss: 1.5574... Generator Loss: 0.5681
Epoch 0/1... Discriminator Loss: 1.5269... Generator Loss: 0.6107
Epoch 0/1... Discriminator Loss: 1.6962... Generator Loss: 0.6056
Epoch 0/1... Discriminator Loss: 1.5061... Generator Loss: 0.6133
Epoch 0/1... Discriminator Loss: 1.4772... Generator Loss: 0.6358
Epoch 0/1... Discriminator Loss: 1.6195... Generator Loss: 0.5473
Epoch 0/1... Discriminator Loss: 1.5288... Generator Loss: 0.6228
Epoch 0/1... Discriminator Loss: 1.5740... Generator Loss: 0.6372
Epoch 0/1... Discriminator Loss: 1.5141... Generator Loss: 0.6371
Epoch 0/1... Discriminator Loss: 1.5410... Generator Loss: 0.5974
Epoch 0/1... Discriminator Loss: 1.7063... Generator Loss: 0.4732
Epoch 0/1... Discriminator Loss: 1.5304... Generator Loss: 0.5643
Epoch 0/1... Discriminator Loss: 1.6508... Generator Loss: 0.5397
Epoch 0/1... Discriminator Loss: 1.5691... Generator Loss: 0.6132
Epoch 0/1... Discriminator Loss: 1.5570... Generator Loss: 0.5791
Epoch 0/1... Discriminator Loss: 1.4948... Generator Loss: 0.6242
Epoch 0/1... Discriminator Loss: 1.6423... Generator Loss: 0.5070
Epoch 0/1... Discriminator Loss: 1.5240... Generator Loss: 0.5633
Epoch 0/1... Discriminator Loss: 1.4885... Generator Loss: 0.6723
Epoch 0/1... Discriminator Loss: 1.6090... Generator Loss: 0.5799
Epoch 0/1... Discriminator Loss: 1.5613... Generator Loss: 0.5842
Epoch 0/1... Discriminator Loss: 1.5342... Generator Loss: 0.6056
Epoch 0/1... Discriminator Loss: 1.5713... Generator Loss: 0.6097
Epoch 0/1... Discriminator Loss: 1.4643... Generator Loss: 0.6869
Epoch 0/1... Discriminator Loss: 1.5351... Generator Loss: 0.6722
Epoch 0/1... Discriminator Loss: 1.5034... Generator Loss: 0.5425
Epoch 0/1... Discriminator Loss: 1.3814... Generator Loss: 0.7424
Epoch 0/1... Discriminator Loss: 1.4742... Generator Loss: 0.5958
Epoch 0/1... Discriminator Loss: 1.5126... Generator Loss: 0.5581
Epoch 0/1... Discriminator Loss: 1.4773... Generator Loss: 0.6680
Epoch 0/1... Discriminator Loss: 1.4012... Generator Loss: 0.6620
Epoch 0/1... Discriminator Loss: 1.5049... Generator Loss: 0.6576
Epoch 0/1... Discriminator Loss: 1.4551... Generator Loss: 0.6239
Epoch 0/1... Discriminator Loss: 1.5878... Generator Loss: 0.5196
Epoch 0/1... Discriminator Loss: 1.4784... Generator Loss: 0.6237
Epoch 0/1... Discriminator Loss: 1.5070... Generator Loss: 0.6108
Epoch 0/1... Discriminator Loss: 1.5091... Generator Loss: 0.5785
Epoch 0/1... Discriminator Loss: 1.4981... Generator Loss: 0.5861
Epoch 0/1... Discriminator Loss: 1.4774... Generator Loss: 0.5580
Epoch 0/1... Discriminator Loss: 1.5384... Generator Loss: 0.7106
Epoch 0/1... Discriminator Loss: 1.4121... Generator Loss: 0.7205
Epoch 0/1... Discriminator Loss: 1.3623... Generator Loss: 0.6747
Epoch 0/1... Discriminator Loss: 1.4843... Generator Loss: 0.6813
Epoch 0/1... Discriminator Loss: 1.5804... Generator Loss: 0.6267
Epoch 0/1... Discriminator Loss: 1.5288... Generator Loss: 0.6308
Epoch 0/1... Discriminator Loss: 1.4532... Generator Loss: 0.6450
Epoch 0/1... Discriminator Loss: 1.3684... Generator Loss: 0.6681
Epoch 0/1... Discriminator Loss: 1.6738... Generator Loss: 0.5361
Epoch 0/1... Discriminator Loss: 1.3834... Generator Loss: 0.6963
Epoch 0/1... Discriminator Loss: 1.4211... Generator Loss: 0.6272
Epoch 0/1... Discriminator Loss: 1.4413... Generator Loss: 0.6182
Epoch 0/1... Discriminator Loss: 1.4887... Generator Loss: 0.5687
Epoch 0/1... Discriminator Loss: 1.6981... Generator Loss: 0.4714
Epoch 0/1... Discriminator Loss: 1.4011... Generator Loss: 0.7470
Epoch 0/1... Discriminator Loss: 1.5981... Generator Loss: 0.5701
Epoch 0/1... Discriminator Loss: 1.2407... Generator Loss: 0.8206
Epoch 0/1... Discriminator Loss: 1.4557... Generator Loss: 0.7303
Epoch 0/1... Discriminator Loss: 1.4618... Generator Loss: 0.6335
Epoch 0/1... Discriminator Loss: 1.5291... Generator Loss: 0.5872
Epoch 0/1... Discriminator Loss: 1.5042... Generator Loss: 0.5277
Epoch 0/1... Discriminator Loss: 1.4165... Generator Loss: 0.6625
Epoch 0/1... Discriminator Loss: 1.4141... Generator Loss: 0.6310
Epoch 0/1... Discriminator Loss: 1.5540... Generator Loss: 0.5669
Epoch 0/1... Discriminator Loss: 1.4031... Generator Loss: 0.6027
Epoch 0/1... Discriminator Loss: 1.6170... Generator Loss: 0.5025
Epoch 0/1... Discriminator Loss: 1.4086... Generator Loss: 0.6141
Epoch 0/1... Discriminator Loss: 1.6845... Generator Loss: 0.4742
Epoch 0/1... Discriminator Loss: 1.5402... Generator Loss: 0.5127
Epoch 0/1... Discriminator Loss: 1.6586... Generator Loss: 0.4736
Epoch 0/1... Discriminator Loss: 1.6653... Generator Loss: 0.4207
Epoch 0/1... Discriminator Loss: 1.5976... Generator Loss: 0.5539
Epoch 0/1... Discriminator Loss: 1.3295... Generator Loss: 0.6985
Epoch 0/1... Discriminator Loss: 1.5045... Generator Loss: 0.6438
Epoch 0/1... Discriminator Loss: 1.5351... Generator Loss: 0.5368
Epoch 0/1... Discriminator Loss: 1.6151... Generator Loss: 0.4921
Epoch 0/1... Discriminator Loss: 1.3131... Generator Loss: 0.6788
Epoch 0/1... Discriminator Loss: 1.6263... Generator Loss: 0.4625
Epoch 0/1... Discriminator Loss: 1.5561... Generator Loss: 0.4519
Epoch 0/1... Discriminator Loss: 1.5548... Generator Loss: 0.4963
Epoch 0/1... Discriminator Loss: 1.5896... Generator Loss: 0.4756
Epoch 0/1... Discriminator Loss: 1.5817... Generator Loss: 0.5390
Epoch 0/1... Discriminator Loss: 1.5673... Generator Loss: 0.5707
Epoch 0/1... Discriminator Loss: 1.5221... Generator Loss: 0.5679
Epoch 0/1... Discriminator Loss: 1.5549... Generator Loss: 0.4610
Epoch 0/1... Discriminator Loss: 1.5089... Generator Loss: 0.5458
Epoch 0/1... Discriminator Loss: 1.7347... Generator Loss: 0.3592
Epoch 0/1... Discriminator Loss: 1.5929... Generator Loss: 0.5044
Epoch 0/1... Discriminator Loss: 1.6135... Generator Loss: 0.4762
Epoch 0/1... Discriminator Loss: 1.8747... Generator Loss: 0.3511
Epoch 0/1... Discriminator Loss: 1.7488... Generator Loss: 0.3951
Epoch 0/1... Discriminator Loss: 1.4602... Generator Loss: 0.5151
Epoch 0/1... Discriminator Loss: 1.4131... Generator Loss: 0.5839
Epoch 0/1... Discriminator Loss: 1.5566... Generator Loss: 0.5060
Epoch 0/1... Discriminator Loss: 1.5159... Generator Loss: 0.5248
Epoch 0/1... Discriminator Loss: 1.7019... Generator Loss: 0.4792
Epoch 0/1... Discriminator Loss: 1.5938... Generator Loss: 0.5745
Epoch 0/1... Discriminator Loss: 1.5691... Generator Loss: 0.4409
Epoch 0/1... Discriminator Loss: 1.7825... Generator Loss: 0.3662
Epoch 0/1... Discriminator Loss: 1.5671... Generator Loss: 0.4647
Epoch 0/1... Discriminator Loss: 1.7083... Generator Loss: 0.4081
Epoch 0/1... Discriminator Loss: 1.7925... Generator Loss: 0.3679

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.